Construction and validation of GPR55 active and inactive state in silico models through the use of biological assays, mutation data, and structure activity relationships

G-protein coupled receptors (GPCRs) function as both gatekeepers and molecular messengers of the cell. They relay signals that span the cell membrane mediating nearly every significant physiological process and currently represent the target of about 30% of all drugs. The signals they transmit can arise from a remarkable variety of stimuli which includes, but is not limited to, photons, neurotransmitters and hormones. GPR55, a rhodopsin-like (Class A) GPCR, has received a great deal of attention due to its emerging involvement in a multitude of physiological processes and its putative identity as a third type of cannabinoid receptor. Characterizations of GPR55 knock-out mice reveal a role for the receptor in controlling inflammatory pain, neuropathic pain, and bone resorption.1 Myriad other studies indicate that GPR55 activation may play a part in oncogenesis and metathesis. GPR55 can be found in numerous tissue types throughout the body and is also highly expressed throughout the cerebellum and surrounding central nervous system lending credence to the idea that this receptor may play a more crucial physiological role than originally thought.2 GPR55 has an extensive physiological profile and has been shown to respond uniquely to a great number of diverse compounds. Specifically, it has been shown to recognize many cannabinoid compounds, including CB1 and CB2 endogenous ligands, phytocannabinoids and synthetic cannabinoids. Similar to the ligands of the CB1 and CB2 receptors, the endogenous ligand of GPR55, lysophosphatidylinositol (LPI), is a lipid-derived molecule.3 LPI activates ERK1/2 and increases [Ca2+] and, to date, there has been no evidence that LPI interacts with the other cannabinoid receptors. Despite innumerable prospective clinical uses hinted at by the aforementioned research no low nanomolar potency ligands of GPR55 have been identified. Nor has there been a radio-ligand developed to characterize the binding site of this receptor. Lack of such tools is a great impediment to any forward progress towards developing the GPR55 receptor as a therapeutic target for drug design. The following research details the creation of both a GPR55 active- and a GPR55 inactive- state homology model. Towards this goal, Chapter I details the background of the discovery, pharmacological relevance and ligand scope of GPR55. Its purpose is to establish a framework for the research that follows and highlight the medical importance of this elusive receptor. Chapter II describes the synthetic preparation of antagonists of GPR55 for use in preliminary SAR studies. The original high throughput screen that lead to the identification of novel GPR55 scaffold chemotypes from the screening of over 300,000 compounds gave rise to the piperidinyloxadiazolone compound CID23612552 and the synthetic diversification of what was then dubbed Scaffold 1. A detailed description of the methods used in the construction of the updated R and R* state of GPR55 models is handled in Chapter III. A combination of Conformational Memories4,5 (using the CHARMM forcefield), Ligand Conformational Analysis (performed using Spartan (Wavefunction, Inc., Irvine, CA)) and Macromodel/Maestro/Glide (from the Schrödinger suite) was used to build and refine both GPR55 model states. Chapter IV then covers model validation and refinement. Using the phenylpiperazine (ML184 CID2440433) and mutations performed in the lab of Dr. Mary Abood (Temple University) it was shown that the current iteration of the GPR55 R* model was indeed a valid representation of the activated state of this receptor. This chapter also provides information that gives rise to the “Future Directions” chapter, Chapter V. This final chapter is a look forward to the research that still remains to be done to ensure that these models will function as the accurate tools that they have the potential to be. We used the GPR55 R bundle to suggest antagonist structures that will maximize ligand/receptor interactions and hopefully give rise to nanomolar potency molecules. These ligands will need to be synthesized and tested. We also identified key residues in the active bundle (GPR55 R*) that could be mutated to enhance or verify ligand binding. Mutations that destroy receptor function, while interesting, would not have the same utility as the aforementioned kinds of mutations

Identification of Crucial Amino Acid Residues Involved in Agonist Signaling at the GPR55 Receptor

GPR55 is a newly deorphanized class A G-protein-coupled receptor that has been implicated in inflammatory pain, neuropathic pain, metabolic disorder, bone development, and cancer. Few potent GPR55 ligands have been identified to date. This is largely due to an absence of information about salient features of GPR55, such as residues important for signaling and residues implicated in the GPR55 signaling cascade. The goal of this work was to identify residues that are key for the signaling of the GPR55 endogenous ligand, l-α-lysophosphatidylinositol (LPI), as well as the signaling of the GPR55 agonist, ML184 {CID 2440433, 3-[4-(2,3-dimethylphenyl)­piperazine-1-carbonyl]-<i>N</i>,<i>N</i>-dimethyl-4-pyrrolidin-1-ylbenzenesulfonamide}. Serum response element (SRE) and serum response factor (SRF) luciferase assays were used as readouts for studying LPI and ML184 signaling at the GPR55 mutants. A GPR55 R* model based on the recent δ-opioid receptor (DOR) crystal structure was used to interpret the resultant mutation data. Two residues were found to be crucial for agonist signaling at GPR55, K2.60 and E3.29, suggesting that these residues form the primary interaction site for ML184 and LPI at GPR55. Y3.32F, H(170)­F, and F6.55A/L mutation results suggested that these residues are part of the orthosteric binding site for ML184, while Y3.32F and H(170)­F mutation results suggest that these two residues are part of the LPI binding pocket. Y3.32L, M3.36A, and F6.48A mutation results suggest the importance of a Y3.32/M3.36/F6.48 cluster in the GPR55 signaling cascade. C(10)­A and C(260)­A mutations suggest that these residues form a second disulfide bridge in the extracellular domain of GPR55, occluding ligand extracellular entry in the TMH1–TMH7 region of GPR55. Taken together, these results provide the first set of discrete information about GPR55 residues important for LPI and ML184 signaling and for GPR55 activation. This information should aid in the rational design of next-generation GPR55 ligands and the creation of the first high-affinity GPR55 radioligand, a tool that is sorely needed in the field

Ebola Virus Bayesian Machine Learning Models Enable New in Vitro Leads

We have previously described the first Bayesian machine learning models from FDA-approved drug screens, for identifying compounds active against the Ebola virus (EBOV). These models led to the identification of three active molecules in vitro: tilorone, pyronaridine, and quinacrine. A follow-up study demonstrated that one of these compounds, tilorone, has 100% in vivo efficacy in mice infected with mouse-adapted EBOV at 30 mg/kg/day intraperitoneal. This suggested that we can learn from the published data on EBOV inhibition and use it to select new compounds for testing that are active in vivo. We used these previously built Bayesian machine learning EBOV models alongside our chemical insights for the selection of 12 molecules, absent from the training set, to test for in vitro EBOV inhibition. Nine molecules were directly selected using the model, and eight of these molecules possessed a promising in vitro activity (EC50 < 15 μM). Three further compounds were selected for an in vitro evaluation because they were antimalarials, and compounds of this class like pyronaridine and quinacrine have previously been shown to inhibit EBOV. We identified the antimalarial drug arterolane (IC50 = 4.53 μM) and the anticancer clinical candidate lucanthone (IC50 = 3.27 μM) as novel compounds that have EBOV inhibitory activity in HeLa cells and generally lack cytotoxicity. This work provides further validation for using machine learning and medicinal chemistry expertize to prioritize compounds for testing in vitro prior to more costly in vivo tests. These studies provide further corroboration of this strategy and suggest that it can likely be applied to other pathogens in the future

Characterization of new, efficient Mycobacterium tuberculosis topoisomerase-I inhibitors and their interaction with human ABC multidrug transporters

Drug resistant tuberculosis (TB) is a major worldwide health problem. In addition to the bacterial mechanisms, human drug transporters limiting the cellular accumulation and the pharmacological disposition of drugs also influence the efficacy of treatment. Mycobacterium tuberculosis topoisomerase-I (MtTopo-I) is a promising target for antimicrobial treatment. In our previous work we have identified several hit compounds targeting the MtTopo-I by in silico docking. Here we expand the scope of the compounds around three scaffolds associated with potent MtTopo-I inhibition. In addition to measuring the effect of newly generated compounds on MtTopo-I activity, we characterized the compounds' antimicrobial activity, toxicity in human cells, and interactions with human multidrug transporters. Some of the newly developed MtTopo-I inhibitors have strong antimicrobial activity and do not harm mammalian cells. Moreover, our studies revealed significant human ABC drug transporter interactions for several MtTopo-I compounds that may modify their ADME-Tox parameters and cellular effects. Promising new drug candidates may be selected based on these studies for further anti-TB drug development